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annotate toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/learn_square_hhmm_cts.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 % Try to learn a 3 level HHMM similar to mk_square_hhmm
wolffd@0 2 % from hand-drawn squares.
wolffd@0 3
wolffd@0 4 % Because startprob should be shared for t=1:T,
wolffd@0 5 % but in the DBN is shared for t=2:T, we train using a single long sequence.
wolffd@0 6
wolffd@0 7 discrete_obs = 0;
wolffd@0 8 supervised = 1;
wolffd@0 9 obs_finalF2 = 0;
wolffd@0 10 % It is not possible to observe F2 if we learn
wolffd@0 11 % because the update_ess method for hhmmF_CPD and hhmmQ_CPD assume
wolffd@0 12 % the F nodes are always hidden (for speed).
wolffd@0 13 % However, for generating, we might want to set the final F2=true
wolffd@0 14 % to force all subroutines to finish.
wolffd@0 15
wolffd@0 16 seed = 1;
wolffd@0 17 rand('state', seed);
wolffd@0 18 randn('state', seed);
wolffd@0 19
wolffd@0 20 bnet = mk_square_hhmm(discrete_obs, 0);
wolffd@0 21
wolffd@0 22 ss = 6;
wolffd@0 23 Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6;
wolffd@0 24 Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3];
wolffd@0 25 Qsizes = [2 4 1];
wolffd@0 26
wolffd@0 27 if supervised
wolffd@0 28 bnet.observed = [Q1 Q2 Onode];
wolffd@0 29 else
wolffd@0 30 bnet.observed = [Onode];
wolffd@0 31 end
wolffd@0 32
wolffd@0 33 if obs_finalF2
wolffd@0 34 engine = jtree_dbn_inf_engine(bnet);
wolffd@0 35 % can't use ndx version because sometimes F2 is hidden, sometimes observed
wolffd@0 36 error('can''t observe F when learning')
wolffd@0 37 else
wolffd@0 38 if supervised
wolffd@0 39 engine = jtree_ndx_dbn_inf_engine(bnet);
wolffd@0 40 else
wolffd@0 41 engine = jtree_hmm_inf_engine(bnet);
wolffd@0 42 end
wolffd@0 43 end
wolffd@0 44
wolffd@0 45 load 'square4_cases' % cases{seq}{i,t} for i=1:ss
wolffd@0 46 %plot_square_hhmm(cases{1})
wolffd@0 47 %long_seq = cat(2, cases{:});
wolffd@0 48 train_cases = cases(1:2);
wolffd@0 49 long_seq = cat(2, train_cases{:});
wolffd@0 50 if ~supervised
wolffd@0 51 T = size(long_seq,2);
wolffd@0 52 for t=1:T
wolffd@0 53 long_seq{Q1,t} = [];
wolffd@0 54 long_seq{Q2,t} = [];
wolffd@0 55 end
wolffd@0 56 end
wolffd@0 57 [bnet2, LL, engine2] = learn_params_dbn_em(engine, {long_seq}, 'max_iter', 2);
wolffd@0 58
wolffd@0 59 eclass = bnet2.equiv_class;
wolffd@0 60 CPDO=struct(bnet2.CPD{eclass(Onode,1)});
wolffd@0 61 mu = CPDO.mean;
wolffd@0 62 Sigma = CPDO.cov;
wolffd@0 63 CPDO_full = CPDO;
wolffd@0 64
wolffd@0 65 % force diagonal covs after training
wolffd@0 66 for k=1:size(Sigma,3)
wolffd@0 67 Sigma(:,:,k) = diag(diag(Sigma(:,:,k)));
wolffd@0 68 end
wolffd@0 69 bnet2.CPD{6} = set_fields(bnet.CPD{6}, 'cov', Sigma);
wolffd@0 70
wolffd@0 71 if 0
wolffd@0 72 % visualize each model by concatenating means for each model for nsteps in a row
wolffd@0 73 nsteps = 5;
wolffd@0 74 ev = cell(ss, nsteps*prod(Qsizes(2:3)));
wolffd@0 75 t = 1;
wolffd@0 76 for q2=1:Qsizes(2)
wolffd@0 77 for q3=1:Qsizes(3)
wolffd@0 78 for i=1:nsteps
wolffd@0 79 ev{Onode,t} = mu(:,q2,q3);
wolffd@0 80 ev{Q2,t} = q2;
wolffd@0 81 t = t + 1;
wolffd@0 82 end
wolffd@0 83 end
wolffd@0 84 end
wolffd@0 85 plot_square_hhmm(ev)
wolffd@0 86 end
wolffd@0 87
wolffd@0 88 % bnet3 is the same as the learned model, except we will use it in testing mode
wolffd@0 89 if supervised
wolffd@0 90 bnet3 = bnet2;
wolffd@0 91 bnet3.observed = [Onode];
wolffd@0 92 engine3 = hmm_inf_engine(bnet3);
wolffd@0 93 %engine3 = jtree_ndx_dbn_inf_engine(bnet3);
wolffd@0 94 else
wolffd@0 95 bnet3 = bnet2;
wolffd@0 96 engine3 = engine2;
wolffd@0 97 end
wolffd@0 98
wolffd@0 99 if 0
wolffd@0 100 % segment whole sequence
wolffd@0 101 mpe = calc_mpe_dbn(engine3, long_seq);
wolffd@0 102 pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []);
wolffd@0 103 end
wolffd@0 104
wolffd@0 105 % segment each sequence
wolffd@0 106 test_cases = cases(3:4);
wolffd@0 107 for i=1:2
wolffd@0 108 ev = test_cases{i};
wolffd@0 109 T = size(ev, 2);
wolffd@0 110 for t=1:T
wolffd@0 111 ev{Q1,t} = [];
wolffd@0 112 ev{Q2,t} = [];
wolffd@0 113 end
wolffd@0 114 %mpe = calc_mpe_dbn(engine3, ev);
wolffd@0 115 mpe = find_mpe(engine3, ev)
wolffd@0 116 subplot(1,2,i)
wolffd@0 117 plot_square_hhmm(mpe)
wolffd@0 118 %pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []);
wolffd@0 119 q1s = cell2num(mpe(Q1,:));
wolffd@0 120 h = hist(q1s, 1:Qsizes(1));
wolffd@0 121 map_q1 = argmax(h);
wolffd@0 122 str = sprintf('test seq %d is of type %d\n', i, map_q1);
wolffd@0 123 title(str)
wolffd@0 124 end
wolffd@0 125
wolffd@0 126
wolffd@0 127 if 0
wolffd@0 128 % Estimate gotten by couting transitions in the labelled data
wolffd@0 129 % Note that a self transition shouldnt count if F2=off.
wolffd@0 130 Q2ev = cell2num(ev(Q2,:));
wolffd@0 131 Q2a = Q2ev(1:end-1);
wolffd@0 132 Q2b = Q2ev(2:end);
wolffd@0 133 counts = compute_counts([Q2a; Q2b], [4 4]);
wolffd@0 134 end
wolffd@0 135
wolffd@0 136 eclass = bnet2.equiv_class;
wolffd@0 137 CPDQ1=struct(bnet2.CPD{eclass(Q1,2)});
wolffd@0 138 CPDQ2=struct(bnet2.CPD{eclass(Q2,2)});
wolffd@0 139 CPDQ3=struct(bnet2.CPD{eclass(Q3,2)});
wolffd@0 140 CPDF2=struct(bnet2.CPD{eclass(F2,1)});
wolffd@0 141 CPDF3=struct(bnet2.CPD{eclass(F3,1)});
wolffd@0 142
wolffd@0 143
wolffd@0 144 A=add_hhmm_end_state(CPDQ2.transprob, CPDF2.termprob(:,:,2));
wolffd@0 145 squeeze(A(:,1,:));
wolffd@0 146 CPDQ2.startprob;
wolffd@0 147
wolffd@0 148 if 0
wolffd@0 149 S=struct(CPDF2.sub_CPD_term);
wolffd@0 150 S.nsamples
wolffd@0 151 reshape(S.counts, [2 4 2])
wolffd@0 152 end